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研究生:蕭士閔
研究生(外文):Hsiao, Shih-Min
論文名稱:應用模態擴張與非監督式模糊類神經網路於剪力構架之結構勁度修正
論文名稱(外文):Applying Mode Shape Expansion and Unsupervised Fuzzy Neural Network in Structural Stiffness Parameter Updating
指導教授:洪士林洪士林引用關係
指導教授(外文):Hung, Shih-Lin
口試委員:黃炯憲詹君治
口試委員(外文):Huang, Chiung-ShiannJan, Jiun-Chi
口試日期:2018-01-25
學位類別:碩士
校院名稱:國立交通大學
系所名稱:土木工程系所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:88
中文關鍵詞:模型修正最佳化問題非監督式模糊類神經網路不完全量測模態擴張破壞檢測
外文關鍵詞:Model UpdatingOptimizationUnsupervised Fuzzy Neural NetworkIncomplete MeasurementMode Shape ExpansionStructural Damage Detection
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  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:1
本研究提出一個在不完全量測的情況下,能夠有精度及效率的進行剪力構架之結構勁度修正的方法,並應用於結構破壞檢測上。本研究主要分成三個階段,分別為模態擴張不完全量測之模態振形、利用非監督式類神經網路搜尋答案及對結構物進行損壞指標的判定。首先第一階段主要是要修補資訊,對於不完全量測之模態振形引入模態擴張技術修補未知分量,利用一個六層樓之剪力構架系統,驗證模態擴張技術的效果,結果顯示對於低頻模態有相當好的擴張結果。第二階段為使用非監督式模糊類神經網路進行搜尋,演算法在每次迭代的過程中,會令目前找到的最佳解為中心,並在其周圍產生許多候選答案,並以相似度為目標函數,評估候選答案與目標值之間的關係,並選擇與目標值較相近的候選解,以非監督式模糊類神經網路的重心法來求得最佳解,而為了不使過大的模態擴張誤差影響演算法的判斷,修改目標函數為兩種評估標準。第三階段為引入層間損壞指標,以此判斷結構物破壞位置與破壞程度。為了測試研究方法的可行性,以兩組數值案例進行測試,結果表明僅使用低頻模態下能夠找到與期望值差異很小的答案,再以兩組實測案例之剪力構架,結果顯示出能夠得到可接受範圍的答案,且能識別出破壞位置與破壞程度。
In this study, it is proposed that the accuracy and efficiency of updating structural stiffness parameters from measured incomplete modal data, and applied with structural damage index, there are three stage in this method, the first stage is the mode shape expansion technique to compensate the deficiency of incomplete modal data, use 6-story shear frame system to verify the effect of mode shape expansion, the result revealed the mode shape expansion is good on the low-order modes. The second stage is based on an unsupervised fuzzy neural network mode to search solutions by local search with similarity among particles in each iterative, using the similarity to evaluate the examples and choose the good answer. In order to avoid too large errors affect the search of the algorithm, modify the objective function as two evaluation criteria. The third stage is adopted to locate and quantify damage with damage index. To test the feasibility of the method, two numerical model and two experimental model are used, the results show that the proposed approach can successfully and correctly identify the damage locations and quantify damage.
摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 IX
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 研究步驟 4
1.4 論文架構 5
第二章 文獻回顧 6
2.1 模態擴張 6
2.2 損壞指標 9
2.2.1 模態確認指標(MAC) 10
2.2.2 層間損壞指標 10
2.3 系統識別技術 11
2.3.1 隨機子空間識別法 11
2.4 非監督式類神經網路 16
第三章 研究方法 19
3.1 模態擴張技術 19
3.2 相似度 20
3.3 計算流程 21
第四章 數值模擬與分析 21
4.1 建立模型 25
4.2 結構模型破壞檢測 26
4.2.1 模型1 27
4.3.2 模型2 27
第五章 實驗測試與結果 28
5.1 三層樓剪力構架 28
5.2 八層樓剪力構架 29
第六章 結論與未來展望 31
6.1 結論 31
6.2 未來展望 32
參考文獻 33
附表 37
附圖 60
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